Ranking - Answer ranking
How are those dimensions translated
into features?
• Features that relate to the text
quality itself
• Interaction features
(upvotes/downvotes, clicks,
comments…)
• User features (e.g. expertise in topic)

Ranking - Feed
• Features
• Quality of question/answer
• Topics the user is interested on/
knows about
• Users the user is following
• What is trending/popular
• …
• Different temporal windows
• Multi-stage solution with different
“streams”

Related Questions
• Given interest in question A (source) what other
questions will be interesting?
• Not only about similarity, but also “interestingness”
• Features such as:
• Textual
• Co-visit
• Topics
• …
• Important for logged-out use case

User Trust/Expertise Inference
Goal: Infer user’s trustworthiness in relation
to a given topic
• We take into account:
• Answers written on topic
• Upvotes/downvotes received
• Endorsements
• ...
• Trust/expertise propagates through the network
• Must be taken into account by other algorithms

Trending Topics
Goal: Highlight current events that are
interesting for the user
• We take into account:
• Global “Trendiness”
• Social “Trendiness”
• User’s interest
• ...
• Trending topics are a great discovery mechanism

Content Creation Prediction
•
Quora’s algorithms not only optimize for
probability of reading
•
Important to predict probability of a user
answering a question
•
Parts of our system completely rely on
that prediction
• E.g. A2A (ask to answer) suggestions

Conclusions
• At Quora we have not only Big, but also “rich” data
• Our algorithms need to understand and optimize complex aspects
such as quality, interestingness, or user expertise
• We believe ML will be one of the keys to our success
• We have many interesting problems, and many unsolved challenges